Abstract
Knowledge graph embedding technique aims to represent elements in knowledge graph, such as entities and relations, with numerical embedding vectors in semantic spaces. In general, an existing knowledge graph has relatively stable number of entities and directional relations before being updated. Though existing research has utilized relations of entities for link predication in knowledge graph, the relational directivity feature has not been fully exploited. Therefore, this paper proposes a bi-directional relation aware network (BDRAN) for representation learning, mining information based on directivity of relations in existing knowledge graphs. BDRAN leverages an encoder to capture features of entities in different patterns with diverse directional relations in entity representation level and semantic representation level. Besides, decoder is used to simulate interactions between entities and relations for precise representation learning. Experiments are conducted with widely used standard datasets including WN18RR, FB15k-237, NELL-995 and Kinship. The results present the improvement of BDRAN on the datasets, demonstrating the effectiveness of our model for link prediction.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61772146, No. 61772211, No. U1811263) and Natural Science Foundation of Guangdong (No. c20140500000225).
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Hu, K., Liu, H., Zhan, C., Tang, Y., Hao, T. (2020). A Bi-directional Relation Aware Network for Link Prediction in Knowledge Graph. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_22
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